Parameter Setting: The iteration of TWLDA is set to 2500, which consists of 1000 iterations for the topic model in Step 1 and 1500 iterations for xLDA in Step 4 in Section 4.

in summary, the proposed TWLDA consists of four main processes, which are shown as follows: • Step 3: Discounting the number of words • Step 4: Executing xLDA with the discounted values in Step 1, a topic model is executed.


we present a comparison of structured model with existing methods of LSTM-based sentence embedding (Bowman et al., 2015), LSTM with attention (Rocktäschel et al., 2015), Binary Tree-LSTM sentence embedding (our implementation of Tai et al.


They initially introduced a structural classification for MWEs that relies on the differences in compositionality and fixedness between the different MWEs.

we trained a simple multinomial logistic regression classifier on the MH370 dataset on manually evaluated MwE candidates with a 70% true label precision.


One way to do this could be by partitioning data into the constituent languages (Bergsma et al., 2012) as there exist several sophisticated tools for Twitter (Pak and Paroubek, 2010;Ritter et al., 2012;Owoputi et al., 2013;Kong et al., 2014) designed specifically for various languages (Avontuur et al., 2012;Abdul-Mageed et al., 2012;Rehbein, 2013).


a set of vertices up to a depth of k, is denoted as The third phase however iterates over the graph and determines which vertex pairs to examine as we explain below.

most work on MWEs has been focused on standard monolingual corpora.


These representation learning based methods do not rely on any linguistic tools and can be applied to different languages or domains (Feng et al., 2015).

by encoding the word position j into the query representation, we get the probe representation of the query q as follows: where l j is the j-th column vector of the position encoding matrix l according to the query q.


For a sample of 250 entities present in HEADS, WIBI E and WIBI C +H E , one upward path is sampled per entity per taxonomy, for a total of 750 paths.

hEADS shows a superior ability to select meaningful and compact generalizations that account for the polysemy of the templates without sacrificing precision (e.g., People, Families, Ethnic groups and Noble titles for Tomb of [X]).


Following (Francis-Landau et al., 2016), we evaluate the models on 4 different entity linking datasets: i) ACE (Bentivogli et al., 2010): This corpus is from the 2005 evaluation of NIST.

we note that news consists of formally written documents while a majority of the other domains is informal text, making the source and target domains very divergent in terms of vocabulary and styles (Plank and Moschitti, 2013).


In this section, we evaluate the effectiveness of the global features for EL.

this essentially alleviates the data sparseness problem of unseen words/features and helps to extract more effective features for EL in a given dataset (Kalchbrenner et al., 2014;Nguyen et al., 2016a).


, on the other hand, involves the cosine similarities between the representation vectors at multiple granularities of m i and p ij .

regarding the input contexts for the entity mentions and the target candidates, we utilize the window size of 10 for the immediate context c i , and only extract the first 100 words in the documents for d i and b ij .


p in i } be its set of Wikipedia candidate pages (entities) 1 where n i is the number of page candidates for m i .

after Global-RNN is trained, we retrieve the representation vectorsc i for the immediate contexts of the entity mentions in the source and target domains, project them into the 2-dimension space via the t-SNE algorithm and plot them.


(3) Opinion extraction (Pang and Lee, 2008;Yang and Cardie, 2014), i.e., extracting sentiments or opinions and link them to aspects terms.

the matching time per sentence is almost negligible and ranges between 2ms and 3ms per text.


In the second task (rhetorical text classification), the proposed model outperformed all baselines, as well as the handcrafted approach when including location information in the classification process.

due to the assumptions and bias that are inherent in such labelling heuristics, this may result in lower performance.


Our intuition is that type-based expansion introduces semantically related concepts that elevate the score of topically relevant documents.

this being said, it is important to highlight that some objects such as 'Number", "Matrix" and "Set" are considered basic and are never explicitly defined by mathematicians (Ganesalingam, 2008).


On one hand, the performance of Types2X suggests that information coming from the types occurring in the queries alone may not be enough to produce significant improvements in retrieval efficiency.

query types 'levi decomposition', 'parabolic subgroup', 'unipotent', 'irreducible representation','borel subgroup' Added Types 'reducible representation', 'regular element', 'conjugacy class', 'iwasawa decomposition', 'unipotent element', 'cartan decomposition', 'finite-dimensional representation ', 'unitary representation','cartan subgroup', 'centralizer', 'subgroup', 'triangular decomposition', 'jordan decomposition','parabolic subalgebra' TFIDF-Exp terms 'nilpotent', 'shredded', 'semi-simple', 'inflates', 'centralizer', 'pro-', 'puffed','engulfed', 'inflate', 'semisimple' Table 6: Topic 175 (MO post 90038): Text and types in query, types in query and dictionary, expansion types and sample TFIDF-Exp expansion terms (n=5, s = 2).


The conventionality of this text gives rise to many opportunities for the application of NLP to MIR.

they have relatively few relevant documents (only 19.17% have more than 1 relevant documents).


Given a collection of documents as input, the C-Value method identifies multi-word technical terms using both a linguistic and a statistical component.

despite the sophistication of these methods, they leave elements of the natural language of mathematics largely unexploited.


Formula (5) calculates the weight of a term t i in the entire document d across all the term pairs T where t i exists.

we do not use the document structure attribute.


We leave the sequence of words untouched.

if we swap the order of e 1 and e 2 , then the resulting mirror instance "The [burst] e 2 has been caused by water hammer [pressure] e 1 ."


We want to compare our combined CNN model with models that use either RSeq or SDP alone, so we consider three experiment settings: SDP refers to a CNN model that uses only SDP representation of a relation instance, RSeq refers to a CNN model that uses only the RSeq, and Comb refers to our combined model.

most existing NN-based approaches for relation classification are either based on the shortest dependency path or the raw sequence, although these two representations may complement each other.


As the training method, we use two state-of-the-arts deep learning models (Feng et al., 2015;, which apply convolutional neural networks to extract meaningful representations for each question and answer pair (Feng et al., 2015), and utilize bidirectional attentions on the questions and answers to enhance answer selection accuracy .

why do we call this simple strategy an attention mechanism?


Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.

the new question can be viewed as a corrupted version of a (by adding q's tokens into it), and after the subsequent pooling operations, cosine similarities between the original answer's representation and the new corrupted version are computed.


The hybrid structure leverages complementary strengths by IR and the fast model (both in terms of accuracy and speed), as will be discussed in Section 3.4.

these methods typically require extensive parameter (and hyper-parameter) tuning, which gives rise to efficiency issues for large-scale datasets, and potentially makes them less portable across new datasets and domains (as re-tuning is usually required).


For each relational phrase, we retain 20 candidate KB predicates and textual relations to the ILP model.

generally, the RAEs are based on a novel unfolding objective and learn feature vectors for phrases in syntactic trees.


Given the above definitions, our objective function is to maximize the score of entity linking, relation extraction and their coherence among them: where α, β and δ are weighting parameters tuned on development set.

we first apply an open information extractor (Angeli et al., 2015) on the English Wikipedia to construct a repository of <argument 1 , relation, argument 2 > triples, where the arguments are entity phrases found in the input sentence and the relation represents certain relationship between the arguments.


In this case, the KB predicate kr and textual relation tr should be compatible with each other.

the closed predicate vocabulary assumed by the traditional KB-QA paradigm has inherent limitations.


These two models both get better results than prior methods.

besides, RNN can learn the long-term dependencies and the positional relation of features as well as the global features of the whole sentence.


our pooling operation on adjacent words is able to retain the local features and their sequential relations in a sentence.


Previous research on rumour stance classification for tweets has been limited to the tweet as the unit to be classified.

in this work we set out the rumour stance classification that adopts this broader scheme.


It can be seen that contextual tweet information is highly useful under the neural setting also, which is consistent with previous work for the discrete models.

these features are local and can occur in both sarcasm and non-sarcasm tweets, thereby reducing the confidence of the discrete model (as shown in Figure 5) and can cause relatively more mistakes.


General tweets that are non-sarcastic are also obtained using the tweet IDs shared by Rajadesingan et al.


(2015) and Bamman and Smith (2015) followed the method for building a sarcasm corpus.

to the discrete model, the neural model explores low-dimensional dense vectors as input.


Hence, popular approaches consider lexical units to be independent entities.

in his impressive ICLR keynote 3 -Incorporating linguistic priors in network architectures lead to better performance of models.


We introduce a word attention mechanism to quantitatively model such contextual relevance of words with respect to the target entities.

these methods may suffer from two limitations.


(Roth and Yih, 2007) performed 5-fold cross validation on the complete corpus (1441 sentences), while (Miwa and Sasaki, 2014) performed 5-cross validation on the data set, obtained after splitting the corpus.

they are incapable in handling multiple relation instances in a sentence and can not detect corresponding entity mention pairs participating in the relation detected.


Relation classification is defined as the task of predicting the semantic relation between the annotated pairs of nominals (also known as relation arguments).

the two tasks are interdependent and optimising a single network for ER and RC to model the interdependencies in the candidate entity pairs and corresponding relations is achieved via the proposed joint modeling of subtasks and a simple piggybacking approach.


More precisely, our approach makes use of a hybrid implementation of multiple alignment combination within phrase-based SAPE (PB-SAPE) and hierarchical PB-SAPE (HPB-SAPE) and a system combination framework (a multi-engine pipeline) -that combines the best translations from the enhanced PB-SAPE, HPB-SAPE and the raw MT output.

figure 2 depicts the process of generating additional alignments where the solid links represent edit distance based alignments and the dashed links represent the newly established alignments.


Based on sentence level BLEU scores, the evaluation results presented in Table 1 show that 159 out of the 198 translations provided by the SC-APE are of better quality than the GT output.

the values in the table represent how many translations were chosen by each translator for individual systems.


In our experiments, both APE hypotheses -PB-SAPE and HPB-SAPE, and the baseline Google Translate (GT) output are passed on to the system combination framework which produces the final system output (SC-APE).

when the translators work on the SAPE output, T2 is found to be the most productive while T4 is found to be the least productive.


With the overall annotation agreement per variable ranging from 85% to 97.5%, we find that our automatic annotation strategies are very accurate.

we adapt our systems at two levels: First, we create category-specific language models by interpolating our general movie dialogue language model with a language model trained on only the most relevant subset of the bitext's target side.


Although these models are very useful for natural language processing (NLP) tasks, they only provide monolingual information which is not adequate for crosslingual NLP, MT, multilingual text classification etc.

to show the impact of this module we train a simple baseline using Word2Vec.


A narrative event chain is a set of narrative events that share a common participant.

based on this database, we can recommend anecdotes in response to user queries, which represent their interested topics.


Figure 9(a) shows how the relative F-score changes with a filtering threshold from 0.5 to 2 aggregated across all the 18 relations.

the problem of lacking training examples also impacts other aspects of discourse parsing, for example parsing implicit relations.


The main advantage of the SR-parser is that it can train and parse documents in almost linear time (regarding the document length), while the CODRA parser needs cubic time.

for example, in the very popular corpus -Rhetorical Structure Theory Discourse Treebank (RST-DT) (Carlson et al., 2001) which contains 385 documents, the frequency of "Elaboration" is 31.04%, while the frequency of "Summary" is only 0.88%.


Here, man is introduced into the discourse context by an indefinite article, then subsequently referred to by a definite article.

the LogReg model already achieves high performance on named entities, and it is conceivable that with a larger amount of training data, it can approach the performance of the LStM models, because it will see more conventions about named entities or classes of named entities.


The first is the most frequent baseline (Baseline), which labels all noun phrases with the most frequent class of none.

for the former, we define a sample to be the set of tokens from the previous head noun of a noun phrase up to and including the head noun of the current noun phrase.


Such analysis can be done on various levels -global (collection of text), sentence-level (where sentiment is assigned to one full sentence) or aspect-based.

cRFSUITE (Okazaki, 2007) is used to build a cRF classifier in python.


A significant work on negation problem has been done by Zhu et al.

such terms may or may not be explicitly used, but presence of flow can be assumed.


In setup2, 10-fold cross validation is performed on the training set with base model.

also, in order to reduce the size of feature set, only 2000 best bi-grams are selected using Chi-square function 10 .


Discourse information has been very much favored to expand the context around sentiment targets.

the grouping is done only once unlike that described in Figure 5.


A selective approximate parsing is shown in Figure 3.

while we use discourse markers to separate aspect context, we do not restrict neighbor feature embedding based on discourse only.


In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm.

(2013), we consider happy and heartwarming as positive category while sad, angry, boring, and worried as negative category.


This task, which can be complex even for humans (Walker et al., 2012a), is related to argument mining, subjectivity analysis, and sentiment classification.

tweets have restricted length, which sometime makes the author use unstructured or incoherent statements.


This implements a rudimentary type of semantic processing, where the 'meaning' of a sentence is reduced to only one value: its polarity.

the syntactic tree can be re-interpreted in the form of a 'circuit' where the 'signal' flows from one element (or subtree) to another, as shown in Figure 3(c).


Single-Words vs Multiple-Words: Previous concept categorization datasets only contain singlewords.

we use topics as our hierarchical classification problem.


The final version of data contains 5,373,165 entities and 793,856 categories organized as a DAG with a maximum depth of 18.

we add new triplets in the form of (entity, context-entity, entity).


By applying Bayes rule, the full conditional of assigning topic k to z ds is obtained as follows: The possible combinations of i ds is exponential in the length of the sentence s. Similar to (Nguyen et al., 2015), we conduct approximation of the above equation and integrate out i ds , Exactly calculating P (v w |x w ) is computational infeasible, since the normalization term involves all the words in the vocabulary.

by applying LTE, we obtain coherent latent topics and the embedding generated by LTE are effective for identifying topical words of documents.


We utilize Web page dataset for the experiment.

we will be able to drop the normalization factor from the above equation, and simply use e xw•vw in place of P (x w |v w ).


Finally, we removed all categories for which less than 10 terms were found.

they finally aggregated the various similarities by taking the average cosine similarity.


(Bullinaria and Levy, 2007;Bullinaria and Levy, 2012)).

for both datasets, we see that both SVM and MRMf are superior to the nearest centroid classifier.


A well-known effort in this field is that of learning word embeddings for use in a wide range of NLP tasks (Mikolov et al., 2013;Al-Rfou et al., 2013;Levy and Goldberg, 2014;Pennington et al., 2014).

these results are limited by the small size of both training sets.


A popular definition of verbal irony is saying the opposite of what is meant (Grice, 1975).

the past decade has seen an increased research interest in text mining on social media data.


Each document contains three paragraphs: a paragraph with a target VNC, the preceding paragraph and following one.

the inter-annotator agreement was relatively low (Cohen's kappa = .58); therefore, we merged the results keeping only those entries on which the two annotators agreed.


For fair comparison, we also use the same version of dataset as in CDCD and report the results in Appendix A.

different instantiations of R and O will result in the different network structures (Simple RNN, LSTM (Hochreiter and Schmidhuber, 1997), GRU (Cho et al., 2014), etc.).


In this section, we will briefly introduce RNN and CNN and then subsequently describe RNN and CNN based architectures for bi-sequence classification tasks.

task P@200 R@200 F1@200 P@50 R@50 F1@50 P@20 P@10 P@5 AVGP AUC Claim Sentence task table 7: Numbers in Leave-One-Motion-Out mode for all three debating tasks using our approach.


Distributional methods applied to large-sized, often temporally stratified corpora have markedly enhanced the methodological repertoire of both synchronic and diachronic computational linguistics and are getting more and more popular in the Digital Humanities (see Section 2.2).

we are thus skeptical about using word neighborhoods in skip-gram embedding space to adequately capture natural languages' lexical semantics (for English and German, at least).


There are multiple points in our approach which could use either improvement or emphasis.

the similarity measure, to the input phrase, is computed for every word in the lexicon, and the words are ranked according to their similarity measures, leading to the output.


The cases of interest are highlighted in the table.

previous approaches have used just the rank of the target word (which is nevertheless a good indicator of performance), and here we do the same.


(2015) distinguished between two types of attributes: taxonomic and attributive properties.

we employ an off-the-shelf CNN model, as CNNs generally outperform the old approaches such as SIFT, HOG or SURF (LeCun et al., 2015).


The main takeaway is that predictive models, such as Word2Vec (Mikolov et al., 2013), are shown to perform better than count-based ones.

timeML (Pustejovsky et al., 2003) is the widely known annotation framework for creating such representation that was used in the tempEval series, i.e., evaluation exercises focusing on temporal information processing.


predicate-argument structure, as features for the classifiers (Llorens et al., 2010;Laokulrat et al., 2013;D'Souza and Ng, 2013).

our goal is to compare classification performance on temporal relations using traditional features and word embeddings, which have recently shown to achieve good generalisation capabilities in several NLP tasks.


Derczynski and Gaizauskas (2013) show the importance of temporal signals in temporal relation labelling, hence, we include also a similar set of features.

braud and Denis (2015) presented a detailed comparative studies for assessing the benefit of unsupervised word representations, i.e.


In this work, we assess the impact of using word embeddings as features for event words in classifying temporal relations of event-event pairs and event-DCT (document creation time) pairs.

we want to evaluate whether, and in which configurations, they can contribute to advance state-of-the-art performance on temporal relation classification.


Pertinence is computed by various different measures, the most recent of which is balAPinc (Kotlerman et al., 2010), where LIN is Lin's similarity (Lin, 1998) and APinc is an asymmetric measure: APinc applies the DIH via the idea that features with high values in F( − → u ) must also have high values in In the above formula, f r is the feature in F( − → u ) with rank r; P (r) is the precision at rank r; and rel (f r ) is a weight computed as follows: where shows the rank of feature f within the entailed vector.

naturally these models are used extensively to measure the semantic similarity of words (Turney and Pantel, 2010).


We remedy this problem by embedding the missing information into the existing tensors; for example, we can amend the tensors of the projective model as follows: with − → v denoting the distributional vector of the verb.

this kind of lifting of the entailment relationship from words to the phrase/sentence level also holds for tensor-based models (Balkır et al., 2016a).


For instance, the substitution of u for v would not work for sentences that have negations or quantifiers such as 'all' and 'none'.

, + refer to vector element-wise multiplication and addition, respectively, ⊗ proj to the projective tensor models of Section 6.3, and ⊗ rel/frob to the construction of Section 6.1.


The most frequently fixated candidate other than the correct one follows the correct candidate in the ranking.

the number of the correct arguments that directly depend on the target predicate is 35 out of 184 examples, making 19% of the texts.


We conduct experiments on the Stanford Natural Language Inference corpus (SNLI) (Bowman et al., 2015).

we concatenate the forward output h → h i and the backward output h ← h i as the final output of the bidirectional rLSTM: h h i .


They applied a large variety of methods to encode the two sentences (premise and hypothesis), such as LSTM encoder, GRU encoder and tree-based CNN encoder.

(2015) proposed ordered embedding, Mou et al.


The original data set contains 570,152 sentence pairs, each labeled with one of the following relationships: entailment, contradiction, neutral and −, where − indicates a lack of consensus from the human annotators.

this kind of unit is specially designed for dual sentence modeling.


We show our system to also work well on the benchmark Microsoft Paraphrase database for clean text, and thereby empirically establish our approach as the most ubiquitous system available today, across the different types of paraphrase detection datasets (noisy and clean-text).

we empirically observe that the preprocessing step of performing synonym replacement with wordnet, significantly improves the system performance, when deployed along with this feature.


Although sentence intersection has existed for over a decade, it has received little attention due to a lack of annotated data.

each sentence pair was given to 5 workers, of which the most consistent 3 were selected to reduce the effect of poor annotations (agreement between two workers was measured using the ROUGe-like F 1 ).


We observe that for certain dependency types 4 that connect function words to content words as modifiers, the modifiers should always be part of the same alignment as their heads.

if "CNN confirms: X", then "X" is typically true.


This allows for efficient training of deep LSTMs.

the bi-directional model used half of the number of layers shown for other models.


Scores on various metrics vary a lot across the datasets, which is understandable due to their inherent differences.

note that, for the WikiAnswers dataset, we clip the vocabulary size 2 to 50K and use the special UnK symbol for the words outside the vocabulary.


We use Bing translator to get the translation candidates, and employ two experts to annotate the best result among candidate list.

relational (Tensor) Model only use the relational information of a triple.


Along with this approach come two contributions.

such resources are not readily available for most domains, and if so, they could be quickly out of date.


In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges.

3.2 Explanation-centered Analysis For each question, we create gold explanations that describe the inference needed to arrive at the correct answer.


(2013) and includes additional types derived from frequently observed semantic structures in the explanation sentences.

we model the requirements based on appropriate sources of evidence to be used for the QA task.


In our domain, the constant symbols correspond to politicians and predicates to: party affiliation, Twitter activity, and similarities between politicians based on temporal Twitter behaviors.

for general issues, politicians will comment as frequently as desired to express their support or lack thereof for that particular issue.


x j i ∈ R 1×D is the i th document in domain j and represented as a Boolean vector of bag-of-words.

for sentiment analysis of user generated content, traditional works have focused on textual content and dictionaries based approaches (Taboada et al., 2011;Hu and Liu, 2004;Pang and Lee, 2008).


All models, except SVM-ID, SVM-MD, and ClassifierFusion, are implemented through transductive inference to better leverage the unlabeled data.

in addition, we examine the limitation of domain-dependent sentiment expression and investigate efforts on building a generalized representation for all domains.


ClassifierFusion (Li and Zong, 2008): Multi-domain sentiment classification with classifier-level combination.

we repeat the experiment of multi-domain sentiment classification by comparing our Proposed-R(P-R) and SDA with another three state-of-art classifiers: K-Neighbors Classifier(KNC), Gaussian Naive Bayes(GNB) and RandomForest Classifier(RFC).


The general procedure for ICM is very similar to Gibbs sampling.

such an algorithm, while fast, yields extremely poor maximum a posteriori estimates for DP-MOM (Walker, 2012).


We also note that variational inference is inappropriate for this model, as it only achieves good performance when used in conjunction with hyper-parameter optimization.

for example, (2004) found that LDA requires hundreds of iterations of sampling before the log-likelihood of the model stabilizes in distribution.


Our second measure evaluates the diversification produced by a clustering algorithm.

adding human interaction to probabilistic models requires inference algorithms which are both fast and accurate.


These measures can be used by experts or individuals for diagnostic and management purposes, but also in aggregation, for large scale surveys.

a major advantage of MKL compared to SVR is the interpretation of the feature weights.


It has been proven that hashtags are important for many applications in microblogs (Efron, 2010;Bandyopadhyay et al., 2012;Davidov et al., 2010;Wang et al., 2011;Li et al., 2015).

we implement their method for evaluating it on the corpus constructed in this work.


We perform hashtag recommendation as follows.

not all microblog posts have hashtags created by their authors.


pairs of input and output, E = {(x (i) , y (i) ) ∈ X × Y : i = 1 .

in line 3, graph traversal queries (using the ∼> operator) are applied to use an edge and go from a sentence node to all contained predicate nodes in the sentence and then apply the constraint to all of those predicates.


The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

the prediction function h is, h(x; w) = arg maxŷ ∈Y g(x, y; w).


(In the Czech Republic, the presentation of syntax in school is very close to dependency syntax.)

manual annotation is now the place where linguistics hides.


TRAINING is not sufficient to ensure that players annotate data consistently: if they did the TRAINING on a certain relation a long time ago or if they play a lot, their ability to annotate this relation may decrease.

the creation of such resources is notoriously costly, especially when complex annotations, e.g.


In our example, the training data will now consist of following instances: (<w> implies embedding of the English word w to which the in-language word has been translated) Now the word vectors are initialized as before and the network is trained as usual on this extended training data (Figure 1c).

when the labeled dataset is small in size, as is often the case in most languages, this problem adversely affects the classification performance.


We find that both models are able to distinguish semantic equivalents from non-translation pairs.

the first experiment checks whether the learned bilingual semantic similarity is able to improve the translation quality.


Also, it should converge to zero at the end of the sentence.

we first introduce a recurrent attention mechanism with extra recurrent paths as an implicit distortion model to solve the reordering problem.


Table 4 shows the overall alignment results on word alignment task in terms of the metric, alignment error rate.

while our SA-NMT does not advance the state-of-the-art Moses as in large scale translation, this is a strong result if we consider that previous works on low resource translation tasks: Arthur et al.


In order to facilitate incremental decoding, it tries to make this alignment prediction without the information about the target word itself, and thus this attention can be considered to be a form of a reordering model (see §2 for more details).

cSTAR03 is the development set while IWSLT04 is the test set.


As described in §2, the attention model outputs a soft alignment α, such that α t is a normalized probability distribution.

as explained in §2, standard NMT can not use the target word information to predict its aligned source words, and thus might fail to predict the correct source words for some target words.


This paper makes the following contributions: • It revisits the attention model from the point view of reordering ( §2), and propose a supervised attention for NMT that is supervised by statistical alignment models ( §3).

the attention based NMT might not deliver satisfying alignments, as reported in (Cheng et al., 2016), compared to conventional alignment models.


We found the indicators to be helpful also when no adaptation is performed, possibly because it allows a form of multitask learning over the different documents and systems.

• How effective are the proposed features, such as indicator features, predicting deviations, and using black box regression or confidences?


We would like to thank the anonymous reviewers for their helpful suggestions.

at training time, we replace the label y ji for the i-th segment of the j-th document (system output) by y ji − Q is the true overall quality of the j-th document.


Section 4 describes our supersense-based translation models.

we employ a sequential labeller Supersense WN Senses Gloss social 1 give help or assistance; be of service social 6 contribute to the furtherance of body 2 improve the condition of stative 3 be of use stative 4 abstain from doing; always used with a negative change 8 improve; change for the better consumption 5 help to some food; help with food or drink consumption 7 take or use that is based on a Hidden Markov Model (HMM) trained in a discriminative way.


The distribution over alignment jumps is then simply P (A j = i|A j−1 = i ) = P (i − i ).

then, once a selection is made, we sample the next state of the Markov chain, i.e.


It can be shown that, with conditionals as shown in (9), the transition kernel underlying this Gibbs sampling procedure is Harris ergodic (Neal, 2003).

they used IBM models 1 and 2 as alignment components.


A small number of sentences can be manually translated for in-domain parameter tuning, but this ignores most of the available multilingual resource.

there were a few document pairs that yielded several hundred sentence pairs.


Additionally, we reduce class imbalance by further randomly shuffling the source tokens when creating training instances.

finally, Bisazza and federico (2016) survey word reordering in machine translation and categorize languages based on their WALS features.


Our work falls in the former category and extends NNJM.

selfnormalization does not solve the computational cost of training the model.


As an embedding-based approach to bilingual lexicon induction, the model consists of matrices W S ∈ R D×V S and W T ∈ R D×V T , which pack up D-dimensional word embeddings of source and target languages with vocabulary sizes V S and V T , respectively.

then the translations in the target language are queried again in the reverse direction to translate back to the source language, and those that don't match with the original source words are discarded.


With supervision, their EMD program can stand alone to fit training data, while in our approach the EMD shows up as a regularizer in the learning objective.

in this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization.


In this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization.

as shown in our experiment, a simple linear combination of the monolingual term and the seed term is insufficient to provide satisfactory performance.


For Spanish-English and Italian-English, we access Open Multilingual WordNet 10 through NLTK.

no prior research has shown how to connect sense embeddings cross-lingually, unless multilingual lexical ontologies exist (Camacho-Collados et al., 2015).


An example of this restructuring may look as follows: Source Sentence However, the new rules put in place will undoubtedly make it more difficult to exercise the right to vote in 2012 .

compound splitters often employed in the literature generally fall into one of two categories (Fritzinger and Fraser, 2010;Popović et al., 2006): corpus-driven and linguistic.


While it may be advisable to try different hyperparameters, an elaborate tuning of the RNNs was not practical given that it took about one week to train a single hyperparameter configuration for machine translation on the WMT data set on our hardware.

this is intuitively obvious because a fixed-size vocabulary cannot contain the names of all people, places, or organizations.


As a definition for rare words, in this experiment, we call a word to be rare if it appears less that 20 times in the corpus.

bilingual word embedding models like (Zou et al., 2013;Søgaard et al., 2015) train vectors for words in both languages in the same vector space, Hence a translation model can be made by these embeddings and it can be really useful for the task of word alignment.


For all sentence pairs (S, T ): ∀s ∈ S, t ∈ T : The new conditional probabilities are calculated in the maximization step and for making means and covariances for the source words, these probabilities are used as weights for weighted averaging of the vectors: In the initialization of the EM algorithm, for all pairs of words, the probability p(t|s) is equal to 1.

for making the IBM alignments, the tool Giza++ was used (Och and Ney, 2000a).


There were proposed methods to overcome the problem of low-resource languages based on using different word alignment methods and combining the results like in (Xiang et al., 2010).

the distributions of source words are made based on their co-occurrences with the target words, which is a similar situation to initialization of the IBM model 1.


We build a baseline using frequency-based features derived from sentiment words to represent news text.

conceptually, a daily abnormal return represents the performance of a stock that varies from the expectation, normally triggered by an event, and can be positive or negative depending on whether the stock outperforms or underperforms the expected return.


Using SENNA (Collobert et al., 2011), we then identify the predicate's arguments and attach them to the MMP.

hint: think about why your idea changed.).


We identify a subset of the key concepts in the question for use in feature extraction.

this will quicken the pace of much related research.


Over the last few decades, technology has provided us many powerful tools that have completely changed our daily routines.

another approach with a similar goal is entailment of semantic facets (Nielsen et al., 2009).


this system retrieves the nearest neighbour of a new document.


2 Data and methodology We used a large collection of listening items from an international English language proficiency test.

item  The Equated Deltas can only be computed after the item has been administered to a certain number of test takers.


This shows that the item difficulty rating is a challenging task even for experts.

at the same time, this result is also consistent with Beinborn et al.


these 26 features covered three aspects of vocabulary: first, vocabulary diversity measured as type-to-token ratio in item text.


排队 很多 时间 (paidui/queue henduo/much shijian/time) in which 很多 时间 is a word selection error.

the collocation data alone is far from enough, thus we need to combine the dataset with other language resources e.g.


For both languages, our trigger classifier had difficulties with correctly classifying certain frequently-occurring words that are sometimes used as triggers and sometimes not.

future work includes transferring our approach to other NLP tasks and exploring the possibility of incorporating active learning into our approach.


The approach captures the connection between target word and its contexts when generating the representation of a sentence.


Target-dependent sentiment classification is typically regarded as a kind of text classification problem in literature.

according to the meaning given to this role by Harbin Institute of Technology, the contact author of this paper is Bing Qin.


Clearly, as mentioned in the beginning, the design of the relations by Mann and Thompson (1988) suggests that the theory be especially amenable to argumentative text.

nonetheless, we tested two simple features that have been used in subjectivity classification (see above) and that might be relevant here: The presence of modal verbs and of 'argumentative' connectives: those that signal a contrastive or causal (in the wide sense) relation.


Video event detection is a challenging problem in information and multimedia retrieval.

in this paper, we propose a new approach to obtain the relationship between concepts by exploiting the syntactic dependencies between words in the image captions.


However, if the dependency (man, standing) is also detected, it is more likely a "flash mob gathering".

in Figure 2, solid arrows show dependencies included in our vocabulary, while dotted arrows indicate dependencies excluded.


As shown in Table 2, tags are the most predictive features and perform better than education and age.

we sort users according to their average spending in descending order, split users equally into 100 buckets and examine the correlation at the group level.


For LATENT feature base classifier selection, we have compared logistic regression with GBDT and found that GBDT preforms better than logistic regression.

we formulate this task as a typical prediction task: it aims to estimate the user consumption level.


probability vector set) while assign labels for new emerging topics as correctly as possible.

(2011) uses Wikipedia article titles as candidate labels, which is hard to assign correct labels for the new emerging topics in Twitter because there may not be the timely articles about the new emerging topics in Wikipedia.


In this paper, we study this fundamental problem which most topic models suffer from, and propose a labeling framework to rapidly label a topic while assign labels for new emerging topics as correctly as possible.

oLLDA only processes the new coming document and update parameters.


These existing approaches generally take following steps to generate meaningful labels for a given topic: (1) extract candidate labels; (2) rank candidate labels.

to the best of our knowledge, no existing method has been proposed to satisfy the demand, except that the simplest method which uses top-n words in the distribution to interpret a topic.


Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years.

(3) asyn-B/F: The two sequences are generated asynchronously, that is, we first generate the backward "half" sequence, conditioned on which we then generate the forward "half."


They were told that instructions for the pink boxes would be given to them later.

the finding that self-repairs are strong indicators of collaborative processes and are increasingly utilized in effective teams, suggests that the detection and interpretation of speech disfluency can be of great benefit to dialogue systems.


Our research hypothesis is that a neural network trained on sequences of questions and their corresponding answers will be able to generate responses that escape the concept of daily-routine expressions, such as "good luck" or "have fun", and facilitate a playground for more detailed and descriptive dialogue utterances.

an elementary linguistics student, he has no choice who is where he is.


Even if our selective standard approach goes against the mainstream which states that adding out-of-domain data decreases the quality of bilingual lexicons, we never denature the initial specialized comparable corpus.

gigaword corpus (gW) is a set of monolingual newswire corpora provided by LDC 6 .


Nonetheless, one type of information has been largely neglected so far: collocations, i.e., idiosyncratic binary lexical co-occurrences.

in order to discover these collocates, we compose the base and the collocate by averaging their respective word embeddings and retrieve its closest words in the vector space according to cosine similarity.


with tuition soaring, and the middle class shrinking along with their incomes, many students and their families are left holding incredibly expensive bags.


Even more clearly, 8.7% of all units in Fox News editorials is testimony evidence, about twice as many on average as in The Guardian (4.55 vs. 2.53).

seven annotators completed all nine editorials, taking around 30 minutes per editorial on average.


While we leave such analyses to future research, we expect that especially the sequence of types of argumentative discourse units in an editorial can be decisive.

it serves as strong evidence that the six proposed types of units cover most units found in editorials.


An entity may be an object (e.g., milk), a being (e.g., Obama or we), or an abstract concept (e.g., learning to cooperate).

similar to the low number of attack relations in persuasive essays (Stab and Gurevych, 2014), we found few insightful patterns of this kind in editorials.


the agent-deriving suffix may have the following 16 forms: -cı, -ci, -cu, -cü, -çı, -çi, -çu, -çü, -ıcı, -ici, -ucu, -ücü, -yıcı, -yici, -yucu, -yücü), and sometimes there is no overt suffix (as in the third person singular copula, which is a zero morpheme).

there has been no apparent progress in the unlabeled attachment score.


Schuster and Paliwal (1997) proposed BLSTM to extend the unidirectional LSTM by introducing a second hidden layer, where the hidden to hidden connections flow in opposite temporal order.

the best accuracy is 52.6 with 2D filter size (5,5) and 2D max pooling size (5,5), this shows that finer tuning can further improve the performance reported here.


We are interested in what is the best 2D filter and max pooling size to get better performance.

the networks will take up more storage space, and consume more time.


The GC corpus contains usage examples and definitions, which are older than the test data and timeless while also being more formal in style, written for a different purpose.

the provenance of the data proved to be more important than the volume.


For word-level word embeddings this means that there are more vectors from the same volume of text, that they are built with less information, and that there are more vectors that are very similar (which complicates the task for a method relying on cosine similarity).

it could be argued that machine-learning-based methods should not be used for benchmarking because they could learn to ignore noise too well.


We achieve this by applying our novel residual bypass function to our character representations, inspired by the success of ResNets.

gRUs  are a recently introduced variant of RNNs, and are designed to prevent vanishing gradients, thus being able to cope with longer input sequences than vanilla RNNs.


Assuming the actual relational structure is sparse, we probably face an imbalanced classification problem, in which we want to identify primarily the minority class.

to predict likely frame relations between frame pairs that are not already listed in the FrameNet), with a fixed a priori confidence threshold in order to focus first on precision of the candidate pairs 1 .


2 Related Work Corpus-based machine learning algorithms have an advantage over knowledge-based ones as they do not involve in human which can be expensive.

first, we compare the performance of TLSA with various parameters, such as the number of singular values and the dimensionality of the 5-grams.


As shown in Table 5, TLSA achieved the best result for the SS task (Pearson correlation) compared with all the baselines and compared methods.


Another productive topic of application is the search for similarities among words.

nouns, verbs, adjectives, adverbs, etc., or giving different nodes and weights to different meanings of a word, and so on.


The best measure in this task is degree centrality that captures almost 48% of Ogden.

dictionaries are rich sources to investigate the semantic structure of natural language.


Our word animacy model achieved an F 1 of 0.98, whereas the prior state of the art achieved F 1 of 0.99 for marking inanimacy.

but word-level animacy is not always sufficient to identify an animate or an inanimate object.


Orǎsan and Evans (2007) did their work in English but their data was not available.

this work was partially supported by Office of Naval Research (ONR) grant number N00014-17-1-2983.


We also marked adjectives suggesting animacy as animate, e.g., alive, vital, kindlier, etc., whereas adjectives implying inanimacy, such as   dead in the noun phrase dead horse, were marked inanimate.

tree is an inanimate word but a talking tree is definitely an animate thing.


We used dependency parses generated by the classic API of Stanford dependency parser (Manning et al., 2014, v3.7.0) to check if the last word of a given referring expression was used as a grammatical subject relative to any verb in the sentence, and encoded this as a boolean feature.

a chair or a book is inanimate because they do not perform any kind of independent action.


As clues, they use both the surface-word and the dependency tree-structure of a sentence.

it could bring with some defects: their model regards all the words in the sentence equally, thus fails in capturing informative parts of the sentence.


The corpus in Section 3 is split into training, development, and evaluation sets, where all models are tuned on the development set and the best models are tested on the evaluation set.

plural mentions make up less than 10% of the dataset, so we are not concerned about these plural-majority clusters, since most if not all clusters would be dominated by singular mentions.


The three decoders work together to generate a 3D binary vector, as in equation 7.

individual features are word tokens and POS tokens for each entity mention.


We also design two special tokens to mark the beginning and end of each mention.

our triad-based model addresses this gap.


The model does not rely on many handcrafted features and is easy to train and use.

figure 2 shows the distribution of entities with respect to their size.


Similarly, POS embeddings are fed into a shared bidirectional LSTM layer.

we do not look to optimize the product of all the potential functions.


LogLinear-C), with 20.1% relative improvement of F1 on English and 12.8% relative improvement on Turkish.

if r ′ is atomic, it is called the final root of w, otherwise, it is called an intermediate root of w. For example, the word realizing can be represented as ((real, -ize, NULL), -ing, DEL-e), where (real, -ize, NULL) represents the word realize.


Our method achieves state-of-theart results on experiments using the Morpho-Challenge data, including English, Turkish, and Finnish.

according to Equation 5, the former one will be kept since it has a higher score.


For Finnish, the best performance of the system is achieved without transformation rules.

secondly, the compounding module can further improve the recall rates and thus decrease the undersegmentation problem for English and Finnish.


p (r, s, t) can be decomposed as follows.


Indigenous languages of the American continent are highly diverse.

the same trend can be observed in SMt for Shipibo-konibo (Galarreta et al., 2017).


(2006) build a dictionary based tool for Quechua and Alva and Oncevay (2017) for Shipibo-Konibo used rule-based analysis and dictionaries.

there are some projects that offer parallel content through a web search interface, e.g., Axolotl (Spanish-Nahuatl parallel corpus) that was mainly gathered from non-digital sources (books from several domains), the documents have dialectal, diachronic and orthographic variation (Gutierrez-Vasques et al., 2016).


We note that using simple keyword matching can result in low coverage due to missing keywords in the bilingual dictionary or word-variations in the target language.

some researchers utilized the bilingual dictionary to translate the models instead (Xu et al., 2016;Littell et al., 2017).


Seeing that the number of documents with at least one positive class is much less common compared to the number of documents without any positive class (see Table 1), simply taking a randomly sampled document from the unlabeled documents will likely give a document with no class, which is less useful compared to document with at least one positive class.

labeling: With the updated set of keywords for each class, we relabeled the documents to obtain a new set of labeled documents and again executed the clustering step to get more keywords.


in resource-poor languages, bilingual dictionaries may be small and sparse.


As part of CELEX-BY-TASK, we additionally evaluate how our models perform on even fewer-50, 100, and 300-training samples on two tasks, 2PKE and 13SIA.

copying x i to the output sequence can be executed by one single action.


(2016) reported, the information of the target predicate w p i propagates to the intermediate prediction h K i,t of the candidate argument w t through the deep bi-RNN by just adding a binary value representing the predicate position.

(2015) jointly identified arguments of multiple predicates by modeling argument interactions with a bipartite graph.


(2017) used Grid RNN to incorporate intermediate representations of the prediction for one predicate generated by an RNN layer into the inputs of the RNN layer for another predicate.

the dropout rate of the GRUs was set to 0.1 from {0.0, 0.1, 0.2}.


Then, we combine the information in the obtained sequence m i,1 , ..., m i,n in a similar manner as in the ATT-POOL model using the attention mechanism, but this time, with self-attention, that is, computing the weights of the elements in the sequence based on the relatedness to the element inside the sequence.

our proposed models extend end-to-end style SRL systems using deep bi-RNN (Zhou and Xu, 2015;He et al., 2017;ouchi et al., 2017) to combine mechanisms that consider multiple predicate interactions.


In this study, we follow the setting of Iida et al.

this annotation task markedly differs from shallow syntactic parsing and is, in fact, more like a semantic role labeling (SRL) task including implicit argument prediction.


In addition, all things being equal, a Wh might generate additional perplexity, since it leads the model to expect a gap at multiple points (cf.

the advantage of the local measure used in this study (P(FS/QM), i.e.


This is quite visible from the ratios, which remain very stable under ACEL, and taper down slightly in PPL.

certain uses of pronouns are standardly treated as bound variables in formal semantics (and there are languages, e.g.


The states that improve the most over baseline, with 15% improvement or more using only textual features are Oregon, Oklahoma, Tennessee, D.C., South Carolina, Louisiana (lower), Georgia (lower), and Alabama (lower).

we further analyzed various factors and their respective importance in the predictive models across the states, gaining a broader understanding of state legislative dynamics.


to aggregate evidence from multiple documents we follow Joshi et al.


Therefore, we define the following reward: where u is the node where the agent is located and u * is the closest tree node that contains the answer (n(u) is the node index as defined above).

word-level embedding For every i = 1, ..., n, every j = 1, ..., m and every k = 1, ..., r, we create an embedding of the input tokens q i , o j , z k in two steps.


DOCQN has longer trajectories, and stops at sentence nodes more frequently, suggesting it reads the document at a finer granularity.

a model that ignores the question and document and always stops in the first paragraph is likely to obtain good performance.


A non-linear softmax layer is deployed behind the FCs.

due to the limitation of literal expression, in general, such event mentions aren't directly connected by "than", but instead the concrete elements in the events are.


We trained the classifier using the training set of each dataset, and evaluated the classification performance using the corresponding test set.

to address this, we use negative sampling (Mikolov et al., 2013b); specifically, we replace E KB in Eq.


We used an MLP classifier with the entity representations as inputs to predict the probability of entity e being a member of type t in the set of possible types T .

the mini-batch size was fixed at 100 and the learning rate was automatically controlled by Adadelta (Zeiler, 2012).


Because the dataset is constructed based on Freebase, we preprocessed the data by mapping each entity to the corresponding entry in Wikipedia and excluded those entities that did not exist in Wikipedia.

we used the accuracy (an entity is correct if all its types and no incorrect types are assigned to it), microaverage F1 (F1 score of all type-entity assignment decisions), and macro-average F1 (F1 score of types assigned to an entity, averaged over entities).


was a staged fake" are strongly related to the historic Soviet space program.

we excluded documents with fewer than five incoming links from other documents if the corresponding entity of the document is not contained in the dataset used in our fine-grained entity typing experiments, presented in Section 3.1.


For example, in Figure 1, NEO estimates the date of a document to be 1958, since it is clear that Sputnik, the most recent word used in the document, sprung into popular use in 1958.

when the regularizer is properly enabled (α = 0.16), observe how the output probability distribution is much smoother and neighboring probability values are more similar.


We further test the performance of the hybrid model that combines our neural model into the featurebased model.

we use policy gradient to directly optimize the solution accuracy, which is more capable for this task.


Such a statement may describe the essence of simplification as a research task, but it hides the fact that it is not always easy to decide what is easy for a particular user.

finally, the sessions table stores each simplification request issued to the backend with a unique session ID, the respective user ID, a time stamp for the session start and one for the submission of feedback, the webpage URL, simplification objects serialized as JSON, the provided rating and finally the frontend version number used in this session.


Our three-stage ETD-M pipeline features a multigraph keyphrase ranking (MGR) algorithm, followed by k-means clustering and keyphrase representation utilizing word embeddings, with a final stage of detecting the topics using an RNN for the final, temporally-sensitive inference of trending topics.

such results allow ETD-M to convey and rank longer, meaningful phrases such as "molecular beam epitaxy" and "metalorganic chemical vapor deposition" within that domain.


• We neuralize the traditional trend detection pipeline.

we use this dataset to evaluate the performance of multigraph ranking (MGR) component as we hypothesize that its mutual recursion will improve trend detection over the baseline TextRank source.


This provides the empirical justification for our assumption when formulating the multi-graph ranking step -that important authors are more likely to cooperate with each other; and that words written by them are more likely to be trending in their respective fields.

after the iterative ranking, noun phrases are assigned a score based on both the adjacency with other phrases and the author information for a particular year.


Using word2vec embeddings performs poorly, too.

as a first step to evaluate our method, we first test whether our entailment vectors learned from labeled word pairs can indeed encode lexical entailment relations.


Many researches have been carried out on fine-grained entity typing in text, which detects local types for an entity mention in a particular plain text from predefined fine-grained entity types (Ling and Weld, 2012;Yogatama et al., 2015;Ren et al., 2016a;Shimaoka et al., 2017;Xin et al., 2018).

p (t|e) is calculated through a twolayer Multi-Layer perceptron (MLp).


It has been confirmed above that our model shows the best performance overall.

in this paper, we follow successful practice in CV and NLP tasks and hypothesize that general and rich semantics learned from unlabeled data can complement task-specific representation learned from labeled data of limited quality.


The supervised learning process learns, from labeled querydocument pairs, discriminative representations adapted for IR.

supervised signals such as click-through data are often limited outside of large industrial research labs, probably due to user privacy concerns.


The cosine similarity of the candidates with the whole sentences as well as the cosine similarity of the candidates with the tri-gram words (one word to the left and one word to the right of the target CP) are used as features.

we have used the LambdaMART algorithm to train our learning and ranking models.


The problem has been extensively studied and a wide range of features has been explored (Stamatatos, 2013;Schwartz et al., 2013;Seroussi et al., 2013;Hürlimann et al., 2015).

bag-of-words approaches have also been reported as being useful for authorship attribution (Koppel et al., 2011).


In addition, writing style between authors in this genre can be very similar.

similar to Grieve, Guthrie (2008) carried out an exploration of 166 features used for authorship attribution including commonly used stylistic features and several others intended to capture emotional tone.


First, adding more unannotated training data is useful, but is subject to diminishing returns.

in this test set, 66.8% of all target words are annotated with the most-frequent sense (MFS) of the lemma.


The 100-billion-token corpus used in the original publication is not publicly available.

(2016), the first operation consists of constructing an LSTM language model to capture the meaning of words in context.


To this purpose, the method relies on another corpus where each word is annotated with the corresponding sense.

we used the model produced at the 65 th epoch for our experiments below.


Graph embedding aims to learn continuous representations of the nodes or edges based on the structure of a graph.

deepwalks (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016) are two representatives of the random walk based methods.


Existing word embedding models (Collobert et al., 2011;Mikolov et al., 2013;Pennington et al., 2014) can be used for readability assessment, but the effectiveness is compromised by the fact that these models typically focus on the syntactic or semantic relations of words, while ignoring the reading difficulty.

from Table  4, the model KEWE h gets the best performance among all the embedding baselines, including SG+KV, which also takes in both knowledge and text corpus.


The ratio N 1 N 1 +N 2 is used to approximate the weighting factor λ in Eq.

we define the word-level difficulty from three perspectives, and use the extracted knowledge to construct a knowledge graph.


The Gutenberg texts have copyright information at the beginning and the end, which we removed using heuristics or manually.

specific features, on the other hand, generated by the last layers, are very dependent on the dataset/task (Yosinski et al., 2014).


CB also makes recommendations that are interpretable (e.g., a book is recommended because the user tends to like a specific genre).

it is therefore a worthwhile endeavor to deploy artificial intelligence techniques to spark people's interest in books by recommending the right type of books.


Due to its several applications, sentential complexity has been a focus of interest in the NLP studies in recent years, such as Vajjala and Meurers (2014), Vajjala and Meurers (2016), Ambati et al.

it occurred 3,777 pairs and 1,774 triplets containing at least two identical sentences, because some of the sentences were simplified only in one level or were not simplified at all (they were considered originally simple).


8), where ˆ∈ (C equals the number of classes) are the scores for each class.

our contributions are summarized as follows: (1) The RHD triplet is first proposed to be the alignment and comparison unit in the neural network for NLI.


Consequently, only words with closely related syntactic/semantic roles (of the aligned predicates) should be compared.

we then concatenate them and use a multi-layer perceptron classifier Q (it has two hidden layers with Relu activation and a softmax output layer) to generate the final overall judgment vector ˆ (as shown in Eq.


The sentence pairs in this corpus are labelled with one of the following relationships: entailment, contradiction, neutral and "-", where "-" means that it lacks of consensus from human annotators.

when comparing two RHD triplets, each part in RHD triplet (i.e., rel, head, and dep) is compared separately and cross-comparison is adopted between nodes.


In the light of these challenges, we propose CORD, a novel COopeRative Denoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances.

besides Corpus-Net, we propose another base network to incorporate information of KG.


The KG-Net predicts wrong relation place of death probably because the appearance of entity Joe Williams.

the Bi-GRU outputs are gathered to s e with entity-level attention, then to S e with bag-level attention, and fed to a Softmax classifier similar to Corpus-Net.


Another interesting find is that it seems a very small amount of supervision (e.g., 500 labeled instances) could significantly help the supervised relation classification system.

it plays a vital role in a variety of natural language processing applications.


Recently, a highly challenging deep QA task is from the Chinese University Admission Examination (Gaokao in Chinese), which is a national-wide standard examination for all senior middle school students in China and has been known as its large scale and strictness.

in addition, all our source for the answer should come from standard textbook which contains unstructured plain texts as we initialized our system building.


Our model selection (tuning) criteria is based on the average of our 3 metrics (SARI, BLEU, 1/FKGL) on the validation set.

input: northern states did not allow slavery , but escaped slaves were returned to their owners as property , so henson would have to flee to canada to be free .


Based on these findings, we share the higher-level layers 2 between the entailment generation and sentence simplification tasks, since they share higher semantic-level language inference skills (for full sentence-to-sentence logical directedness).

(2017) observed that different layers in a sequence-tosequence model (trained on translation) exhibit different functionalities: lower-layers (closer to inputs) of the encoder learn to represent word structure while higher layers (farther from inputs) are more focused on semantics and meanings (Zeiler and Fergus (2014) observed similar findings for convolutional image features).


To identify the evidence span in the utterance chunk, we collect the words of which learnt weight exceeds a certain threshold.

although the particles are not important features of the classification since they appear in any class, it is desirable for dialogue systems to include them into the evidence so that they are used in the later stage of response generation, e.g.


To decide the values for these hyper-parameters, we randomly chose 200 utterance chunks from the training set of surrounding facilities to make a development set, and trained the model with the rest of the training set and evaluated it with the development set.

we replaced the occurrences of numbers and proper nouns with their abstraction symbol NUM and PROP.


Our method is characterized by the incorporation of attention mechanism and attribute-aware representations.

with the selected 10 attributes, we conduct a low-cost annotation over all charges.


(2017) predict the US Supreme Court's decisions with efficient features extracted from case profiles (e.g., dates, locations, terms, and types).

we design ablation test respectively to investigate the effectiveness of these modules.


At the early stage, researchers pay great efforts to extract efficient features from text or case profiles.

these attributes are shared by all charges, and the knowledge can transfer from highfrequency charges to low-frequency ones.


The question, which is closest to the unseen question can be retrieved as a possible semantically similar question.

attention mechanism is also able to align the multi-word expression 'how old' to 'age' as shown in (b)


as it can be observed, the dual-encoder model consistently outperforms the SEQ2SEQ baseline by merely using the SQL query sketch as input.


The novelty resides in generating the underlying SQL sketch first, followed by the generation of the full SQL statement using a dual-encoder model conditioned by both the sketch and the query in natural language.

the main challenge for solving the more complex queries in SENLIDB is the generation of the correct SQL sketch corresponding to a query.


The generated sketch in the second example is incorrect, but the final SQL statement is correct, which means the dualencoder model actually rectified the surface syntactic form provided by the sketch.

a couple of large datasets (Brad et al., 2017;Zhong et al., 2017) have been recently proposed.


In this paper, we propose a novel TMN model for nuclearity recognition in Chinese discourse.

the first is that the majority of the training set is Nucleus-Nucleus, which occupies 56.8% of all annotated nuclearity, while the percentages of the Nucleus-Satellite and Satellite-Nucleus are 25.3% and 17.9%, respectively.


To exhibit the effectiveness of our TMN model, the experiment results consist of two parts: the baselines and TMN.

in this way, the overall discourse semantics can be much better represented.


2013approach the document-level discourse parsing using a model trained by Conditional Random Fields (CRF).

the HILDA discourse parser (Hernault et al., 2010) is the first attempt at document-level discourse parsing on RSt-Dt.


However, in our study, we find that the performance improvement of nuclearity recognition contributes to the improvement of the overall performance.

corresponding to related research based on Rhetorical Structure Theory Discourse Treebank (RST-DT), the micro level is similar to the sentence-level discourse parsing, and the macro level is similar to the document-level discourse parsing.


Second, we propose discourse structure identification and nuclearity recognition models on macro level discourse structure analysis.

in this paper, we concentrate on these two tasks of macro discourse structure, structure identification and nuclearity recognition, and take structure identification as the main task.


Every word accompanies with the various background colors.

it might be classified as a Comparison relation if we only focus on the informative word pair (lucky, earthquake) with contrasting sentiment polarity.


We exploit hierarchical bi-directional LSTMs (Bi-LSTMs) to encode texts, and further enhance the transition-based model with dynamic oracle.

we conduct experiments on a standard RST discourse TreeBank (Carlson et al., 2003).


Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs.

like the previous part, The model setting is also the same as the third row in Table 3 except for the block type and layer number.


(2017) generated a different kind of pseudo-training data.

our model misclassified Ex2 as Expansion.Conjunction even though "yesterday" and "today" were correctly identified as antonyms.


The performance is even worse than RNNSearch* by 10.11 BLEU points.

the present research was supported by the National Natural Science Foundation of China (Grant No.


Our method can also be interpreted as a regularizer to the embedding layer.

from (d), (e), and (f), we confirmed that these features improved the model.


The number of model parameters significantly increases if we add word-level features ((g), (h), (i), (j), (k), and (g)).

here we revisit several interesting properties of SubW BPE .


We refer to our method that incorporates smaller subword features as hierarchical subword features.

our proposed model can make use of both large and small features for correct translations of such rare words.


For the fine-grained analysis, we use instead the expedient of computing error distributions that are normalized with respect to the error counts observed in a bilingual reference system.

in addition to analyzing the behavior of bilingual and multilingual models, mainly, the zero-shot task requires a careful investigation.


Table 6: BLEU score of models on clean and adversarial examples, using a decoder with beam size of 4.

as can be seen in Table 3, targeted attacks are much more difficult with a much lower success rate for the adversary.


Similarly, while FIDS-W has not directly been trained on Key noise, it is trained on a more general type of noise, particularly flip, and thus can perform significantly better on the Key than on other models which also have not been trained on this type of noise.

the evaluation metric for targeted attack is similar to controlled attack with one difference that a perfect push attack produces a translation, t p , which contains words w 1 , w 2 , w t ,..w n , wherein w t has replaced w t .


Black-box attacks often rely on heuristic methods to create adversarial examples.

(2016) for language modeling, and later adapted by Costa-Jussa and Fonollosa (2016) for translation, and is also used by Belinkov and Bisk (2018) for their adversarially-trained models.


src-output In 1931, a woman named Augustine was brought into a medical institution in France.

the best operation for each word is picked locally and independently of other words.


Each entity exists only once in the physical world.

while NILs are a challenge that concerns the long tail of EL, in this work we focus on those entities that have been linked to wikipedia, but are still infrequent, since this provides an entry for extensive analysis of their properties.


PageRank/Random Walk: Boosting (Kulkarni et al., 2009), AGDISTISG (Usbeck et al., 2014), Babelfy (Moro et al., 2014), WAT (Piccinno and Ferragina, 2014), xLisa (Zhang and Rettinger, 2014) and WNED (Guo and Barbosa, 2017) performs PageRank (Page et al., 1999) or random walk (Tong et al., 2006) on the mention-entity graph and use the convergence score for disambiguation.

the main goal of NCEL is to find a solution for collective entity linking using an end-to-end neural model, rather than to improve the measurements of local textual similarity or global mention/entity relatedness.


Particularly, we introduce attention mechanism to robustly model local contextual information by selecting informative words and filtering out the noise.

we set context window to 20, neighbor mention window to 6, and top n = 10 candidates for each mention.


Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions.

we keep both the original string and the transformed string for a semantic unit modified using an operator from the configuration.


We demonstrate the usefulness of the structured representations in two important tasks: entity resolution and relation extraction.

we compute structural similarity c(i, j) of a pair of mentions (i, j) as the edit distance of their structures, S i and S j .


Our method σ outperformed the estimated coverage method with a decrease of 96% in dissimilarity, which makes our method more reliable.

we use σ both during the incremental learning steps from the same sentence pool and to test the model's accuracy/confidence on unseen sentences.


If we calculate on entity level then the score would be 0 but if we calculate on token level, the score is greater than 0.

we evaluated the performance of the FG-NER methods using the test dataset, as described in Table 1.


It first uses Convolutional Neural Network (CNN) to learn character level embeddings in the training process.

there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages.


The proposed multi-order model consists of several single order models (as described in Section 2) of different orders.

the tags inside a segment have internal dependencies.


Our MO-BiLSTM is able to capture the dependencies between two tags, so it can significantly decrease the number of boundary recognition error.

"order" means the number of tags that a prediction involves in a model.


The automatic summarization problem has been studied since the middle of the 20 th century.

motivated by the importance of the summarization task as well as by the promising results of the studies mentioned above, this paper investigates the application of the ACO algorithms to mDS for both the English and Arabic languages.


We have also linearized the dialog trees on Mastodon by splitting and reforming every branch of a dialog tree from the root of the tree as a complete independent dialog.

(2011) exploit affects that are visually expressed on faces to better classify dialog acts.


Despite the popularity of the sentiment task, the problem of developing comprehensive, yet easy to follow and "light" guidelines that would ensure high enough agreement is far from being solved.

we prioritized the speed of annotation over detail, opting for a 3-point scale rather than e.g., the 5point scale in SemEval Twitter datasets (Rosenthal et al., 2017).


8 A user may end a post with a "hedging" emoticon just to express friendliness or politeness, and we found that often to be the case for VK data.

in order to investigate the effects of adding the pre-selected sample to the dataset, we conducted additional 5-way classification experiments using NNC and VK embeddings.


In these preliminary experiments, the best-performing model (NNC) reached an F1 of 0.637 for the positive class vs. the rest and F1 of 0.550 for negative vs. the rest in binary classification.

lexical features have time and again shown inferior performance compared to the supervised learning approaches using annotated data (Gombar et al., 2017), and lack of such a resource severely limits sentiment analysis applications for Russian.


We note that a model with an observed |µ z | >> 0 on a dev set, can be 'corrected' to a large extent (as we show later) by subtracting this dev µ z from the unnormalized scores at test time.

we investigated the following language modeling methods: • DEV-LM -the language model proposed by Devlin et al.


Our results suggest that regularized softmax models perform competitively as long as we are only interested in low test time complexity.

the ability of statistical language models (LMs) to estimate the probability of a word given a context of preceding words, plays an important role in many NLP tasks, such as speech recognition and machine translation.


the improvement in self-normalization seems to occur at the expense of perplexity.


Figure 1: The architecture of position-aware bidirectional attention network for aspect-level sentiment analysis (PBAN).

3We apply a bidirectional attention mechanism, which can enhance the mutual relation between the aspect term and its corresponding sentence, and prevent the irrelevant words from getting more attention.


MemNet: MemNet applies attention multiple times on the word embedding, so that more abstractive evidences could be selected from the external memory.

pBAN not only concentrates on the position information of aspect terms, but also mutually models the relation between aspect term and sentence by employing bidirectional attention mechanism.


Each review contains a list of aspect terms and corresponding polarities, which are labeled with {positive, negative, neutral}.

the main idea of PBAN is to utilize the position embedding of aspect term for calculating the attention weights.


And its corresponding position embedding are obtained by looking up a position embedding matrix P ∈ R dp×N , which is randomly initialized, and updated during the training process.

it can not capture any information of aspect term in sentence .


We also present an experiment with augmented test dataset and demonstrate it helps to understand the model's behavior on locally ambiguous points.


For BLSE, we create mono-domain embeddings using the Word2Vec toolkit 2 by training skip-gram embeddings with 300 dimensions, subsampling of 10 −4 , window of 5, negative sampling of 15 on the concatenation of the unlabeled Amazon corpora.

figure 6: The BLSE model (solid lines) compared to a variant without the target domain projection matrix M (dashed lines).


In this section we examine aspects of our model in an attempt to shed light on its strengths and weaknesses.

our approach is motivated by previous success in learning to project embeddings across languages for cross-lingual sentiment analysis, namely Bilingual Sentiment Embeddings (Barnes et al., 2018, BLSE).


We translated the two last named monolingual datasets in other languages, described below.

since we have no human-created test data for these languages we could only evaluate on machine translations and projected annotations.


If our above strategy fails, i.e., c(s) cannot be projected, e.g., because the words in an L1 component are not aligned to any words in L2, then we simply ignore the projection of c(s) to the L2 sentence, labeling the corresponding words in L2 as non-argumentative instead.

they required about 270 hours to translate the whole PE corpus into German, and the resulting overall cost was roughly 3,000 USD.


Since we have no human-created test data for these languages we could only evaluate on machine translations and projected annotations.

only 6% of the active vocabulary of German in PE DE occurs also in PE EN .


Hence, the message sequence could be represented as Hence, we finally obtain a sequence Y h without OOVs, and HL-EncDec let the hybrid-level Y h to replace the word-level Y .

all models are implemented by Tensoflow.


Given a word x , CharCNN first represents x as a sequence of characters C x = (c 1 , .

there is an issue that the amount of total words is far more than the volume of vocabularies and the out-of-vocabulary words (denoted as OOVs) may appear in the conversations.


Though GloVe vectors are trained on external text corpora, it has become a standard practice for NLP to use pretrained embeddings.

max pooling across filters is applied to get the final output p idc .


Cloze Test is a particular form of reading comprehension task and has been widely adopted as a method for assessing students' language proficiency.

rNNs need to preserve such information over a long distance which is nontrivial.


It combines an attention mechanism with sentiment lexicons based on the sentiment degree.

in such scenarios, several existing methods can be used to construct or extend sentiment lexicons based on large corpora in a semi-supervised way to cover the dataset (Lu et al., 2011;Hamilton et al., 2016).


Empirically, a sentence tends to achieve a higher sentiment degree if it contains a higher proportion of sentiment words.

a it is computed as follows: The learned attention vector a i contains different weights for the corresponding words in sentence S i .


Still, many nouns and phrases do not appear in the training data, making correct predictions difficult.

table 3 shows that GC-BLStM-Attn performs best and significantly better than GC-BLStM on both WordNet and Wikipedia datasets.


BLSTM ends up overfitting to the training data, and its weak generalization power is more evident in the out-domain setting.

we apply our rule-based event detector to generate training data automatically from SemCor.


Events are a key semantic component integral to natural language understanding.

we create an additional dataset which comprises Wikipedia concepts that do not appear in WordNet.


Our training data generated from SemCor is 11 times larger than SW100 with respect to the number of event nuggets.

there is an established consensus that in order to advance natural language applications such as open-domain question answering, we need automatic event identification techniques with larger, wider, and more consistent coverage (Saurí et al., 2005;Pradhan et al., 2007).


Semantic parsers critically rely on accurate and high-coverage lexicons.

as the knowledge base is huge, the searching space is usually quite large.


(2013) learn the lexicon by similar idea, but they use annotated ClueWeb corpus as the text side.

as we do not have enough annotated logical forms to learn the lexicon, and the number of predicates is too large, It is impossible to hand-make the lexicon, To obtain high-quality phrases mappings for each predicate, we construct the seed lexicon in two steps.


Hence, we explore the effectiveness of compositionality of word embeddings in developing a model-free automatic metric to evaluate summary content quality.

we obtain the sense embeddings using the pre-trained model by Rothe and Schütze (2015), that lives in the same vector space as the pre-trained word2vec by Mikolov et al.


We compute the similarity between embeddings of adjacent summary sentences using cosine similarity.

superiority of ROUGE performance to the rest of measures shows that model summaries generated for specific input would still give better indication of important information in the input.


In order to extract finegrained information, automatic systems need to be able to process a diversity of linguistic phenomena such as negation, irony or sarcasm that are used to add extra-propositional meaning.

if we want to advance in the study of this phenomenon, as for most of NLP tasks, the availability of annotated corpora is essential to train algorithms.


In this corpus, syntactic negation was annotated but not lexical nor morphological negation, as in the UAM Spanish Treebank corpus.

each language has specific linguistic resources to express negation and specific negation structures, which should also be reflected in the information annotated in corpora.


Figure 1 shows an example where Bob posts a review about a hotel and gives scores on overall attitude, location, room, and service respectively.

when scoring aspects of a hotel, a business traveler may be critical to service but lenient with price or room.


The analysis of these aspect ratings could not only benefit mining interested aspects for users, but also help companies better understand the major pros and cons of the product.

for aspect a k , we can use a softmax layer to project c k into sentiment distribution p(d, k) over L classes: where p l (d, k) is used to represent the predicted probability of sentiment class l for d based on a k and W lk , b k are parameters of softmax layer for classifying review c k .


Incorporating distributed word embeddings as features has proven effective in a variety of natural language processing tasks, including parsing (Socher et al., 2013), language modeling (Bengio et al., 2003;Mnih and Hinton, 2008) and sentiment analysis (Socher et al., 2011;Labutov and Lipson, 2013;Tang et al., 2014;Tang et al., 2016).

in WNA, each word is associated with only one emotion, therefore a j = 1 if w is associated with that emotion, and a j = 0 otherwise.


The emotion vector a (d) for document d is then, the sum of the emotion vectors of all its words, a (d) = i∈d a (w i ).

balancing the datasets might prove helpful.


A different kind of system was chosen as the object of evaluation in the current study: the Personalized Automated Soccer texts System (hereafter: PASS).

the differences in perceived text quality between the human-written and computer-generated texts might be due to the fact that PASS texts are inspired by texts from GoalGetter -a data-to-text system that attempted to produce a neutral and factual match summary (theune et al., 2001) -in terms of its text structure and the types of data that are incorporated into a text.


Based on this data, a short Dutch match summary of about 50 to 150 words is produced, which is inspired by the reports of the GoalGetter system (Theune et al., 2001).

furthermore, the tailoring aspect was elaborated by tailoring the arguments to the specific challenges that the reader said to face, which is a different kind of tailoring compared to the tailoring of PASS where the style and diction was tailored to fit with the emotions that the reader experiences.


Finding the right reference texts is not the only challenge when evaluating ANLG systems.

the system can be seen as part of the 'Affective' NLG (ANLG) tradition, which aims to produce texts tailored towards the emotional aspects of the intended reader (Mahamood and Reiter, 2011;Ghosh et al., 2017, for instance).


Another reason to think that the PASS-generated texts are effective in their goal is the fact that the intended tailoring was correctly identified by participants during evaluations, to a similar degree as the tailoring in human-texts.

although inspired by previous work, texts by PASS are novel in the sense that the templates have been directly derived from sentences in the MeMo FC corpus (Braun et al., 2016).


Texts that were tailored towards the preferences of the participant were neither seen as more aesthetically pleasing nor as more understandable.

these scores would say little about how well these templates blend together to form an enjoyable and coherent text that displays the information in a sensible way (Scott and Moore, 2007).


Moreover, the lower accuracy results confirmed that there is still scope for improvement in the NAQ detection models.

(b) "null-answer" question detection model: The BiDAF model can be leveraged another way.


This difficulty level would allow researchers to evaluate a machine's NAQ detection performance more precisely.

the created dataset could be effectively utilized in developing the NAQ detection functionality.


Table 7: Example 2 -Exodus 39:6 One of the issues we identified with obfuscation-by-transfer was that of small, localized changes in the input, specifically focussing on words that are most relevant to the adversary.

the idea that computational stylometry might be used to compromise anonymity was first explored by Rao et al.


For the sequence-to-sequence models, → is the primary indicator of successful obfuscation-by-transfer.

see Figure 1 for a visual representations of the base architecture.


One is general purpose, and the the other two are trained by leveraging sentiment information.

the last 6 lines in table 3 detail state-of-the-art results for the four datasets.


Try to present additional requests as soon as you can.

it's impossible to have access to the internet here.


Other work has focused on specific realizations of stylistic variations, such as T-V pronoun selection for translation into German (Sennrich et al., 2016a) or controlling voice (Yamagishi et al., 2016).

try to file any additional motions as soon as you can.


Of course the tool can be used for many other purposes as already mentioned.

because we require back references to be previously declared and have strict rules of scope of visibility enforcing the well-formedness of the search expression we do not experience the strange behaviors reported in (Rohde, 2005) by the expression "crossing link".


In this case it selects only the NP part of the label, not the additional suffix.

we have considered instead, comparing the parsing accuracy of the original parser with that of the parser with the transformations made by tsed.


We used this application in this paper to show that tsed language is sufficiently powerful so that it can be used to replace transformations originally made by programming language code pieces, that were never subject to a restriction in power of expression.

2 We named it tsed after Unix sed, the string transduction tool counterpart to grep.


Automatic evaluation metrics also suggest that adversarial training can improve the diversity of the generated story endings.

seq2seq-Adversarial achieves a mean score of 1.78, which is 21.1% improvement over seq2seq-MLE (1.47).


Because this task essentially has no gold-standard answers, and any reasonable story ending can be the right one.

the dataset is split into training, development and test datasets.


In Section 4 we present the experiments and have discussion.

we divided the task in a neat and intuitive way, avoiding the uncertainty in the merging procedure in traditional copy mechanisms.


Delete-based approaches remove unimportant words from the source sentence and generate a shorter sentence by stitching the remaining fragments together.

we adopt the dataset provided by Toutanova et al.


We can see that the average compression ratios of both our model and the other two baselines are similar.

the model was trained on a set of 2 millions sentence pairs which was constructed by the same approach used in Filippova and Altun (2013).


Following the idea of (Lau et al., 2017) and , we implicitly generate aspects for terms or extract terms for aspects via the auxiliary memory.


Our final loss function is the linear combination of L cla , L pre , L reg : where γ 1 , γ 2 are hyperparameters that control the weight of different parts.

here, the sentiment polarities for "food" and "price" are positive while that for "ambience" is negative.


Working with the same dataset, Badjatiya et al.

we further conducted an analysis of the author embeddings generated by node2vec, in order to validate that they capture the relevant aspects of the community graph.


Subject-matter experts, also known as assessment developers, write the questions that are included in the assessments.

our position is that these are extremely important problems to solve and even limited engagement with and progress towards solving these problems constitutes an important step.


max, min, etc of the word embeddings of the left and right context of the target word, the target itself, and the whole Tweet.

it takes the word vectors of the left, right, target word, and full tweet/sentence/text contexts and performs max, min, average, standard deviation, and product pooling over these contexts to create a feature vector as input to the Support Vector Machine (SVM) classifier.


Neither of these word embeddings are available from the original authors as  never released the embeddings and the link to  embeddings no longer works 8 .

repeatable (replicable and/or reproducible 1 ) experimentation is a core tenet of the scientific endeavour.


Overall, we were able to reproduce the results of all three papers.

(2017) extended the NP work of  and instead of using the full tweet/sentence/text contexts they used the full dependency graph of the target word.


As described earlier, the goal of the Generator and the Selector is not to emulate x with r due to the nature of this research.

this is different from other tasks like aspect-level sentiment classification in Lei et al.


The goal of the Generator gen(x) is to derive a set of binary variables c = {c t } T t=1 by sampling from probability scores s = {s t } T s=1 , where c t ∈ [0, 1] indicates whether x t is chosen as a candidate rationale and s t is computed by the multilevel heuristics.

rewards are fed back to the Generator if the Encoder is able to identify candidate sets that are generated randomly, while the Selector is rewarded if the best performing rationale candidate set outperforms the one in the adversary.


For DesRC(CNN), its F1 score is close to the current-best F1 score very much.

the role of the "intra-sentence" attention for a basic BRCNN model is not as obvious as for a basic CNN model.


We observe that only 42% of goldstandard summary bigrams appear in the source documents, serving as a cap for edge prediction.

we also compare multiple source sentence selection strategies to group source sentences into clusters covering various aspects of the topic; • we conduct extensive experiments on benchmark summarization datasets, and contrast our work with state-of-the-art baselines, including the pointer-generator networks (See et al., 2017).


Minimizing this loss function with respect to θ and φ is straightforward.

• ext-LexRank (Erkan and Radev, 2004) is a graph-based approach that computes sentence importance based on the concept of eigenvector centrality in a graph representation of source sentences; • ext-SumBasic (Vanderwende et al., 2007) is an extractive approach that assumes words occurring frequently in a document cluster have a higher chance of being included in the summary; • ext-KL-Sum (Haghighi and Vanderwende, 2009) describes a method that greedily adds sentences to the summary so long as it decreases the KL divergence; • abs-Opinosis (Ganesan et al., 2010) generates abstractive summaries by searching for salient paths on a word co-occurrence graph created from source documents; Nodes (Oracle) Nodes Edges (Oracle) Edges Approach  P  R  F  P  R  F  P  R  F  P  R  F   LCS 16.7 26.7 19.9 31.5 49.5 37.6 6.7 8.0 6.9 16.1 18.7 16.6 Smatch 20.9 33.2 24.9 33.2 52.0 39.6 9.3 10.7 9.4 17.2 20.1 17.8 Concept Cov.


Suppose that a candidate contains a sequence of m words {w 1 , w 2 , w 3 , .

the solid directed arrows connect the words in the first sentence S1, while the dotted arrows join the words in the second sentence S2.


We also assign this confidence score with the substitution vertex in the word-graph G. Finally, we rank the K candidate fusions and find the N -best paraphrastic sentence fusion which balances the information coverage and the abstractiveness.

rOUGE scores are unfairly biased towards lexical overlap at surface level.


We assume the approximation has the following form: where: the mean µ and standard variance σ are the outputs of the neural network based on the representations of h D and h Y .

the BiLStM consists of forward and backward LStMs, which read the sequence from left-toright and right-to-left to produce both forward and backward sequence of hidden states ( , and the VDANLG architecture which consists of two main components: the VRALStM to generate the sentence and two Critics with an adversarial training procedure to guide the model in domain adaptation.


On the one hand, the VRALSTM models (trained from Scratch or trained adapting model from Source domains) produce the outputs with a diverse range of error types, including missing, misplaced, redundant, wrong slots, or even spelling mistake information, leading to a very high of the slot error rate ERR score.

the portege phosphorus 43 laptop has a 320 gb drive , and is not for business computing .